A light transformer for speech-to-intent applications

Pu Wang; Hugo Van hamme

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arXiv · 2011.12221

A light transformer for speech-to-intent applications

Pu Wang·Hugo Van hamme

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Abstract

Spoken language understanding (SLU) systems can make life more agreeable, safer (e.g. in a car) or can increase the independence of physically challenged users. However, due to the many sources of variation in speech, a well-trained system is hard to transfer to other conditions like a different language or to speech impaired users. A remedy is to design a user-taught SLU system that can learn fully from scratch from users' demonstrations, which in turn requires that the system's model quickly converges after only a few training samples. In this paper, we propose a light transformer structure by using a simplified relative position encoding with the goal to reduce the model size and improve efficiency. The light transformer works as an alternative speech encoder for an existing user-taught multitask SLU system. Experimental results on three datasets with challenging speech conditions prove our approach outperforms the existed system and other state-of-art models with half of the original model size and training time.

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BibTeX RIS

Pu Wang, Hugo Van hamme. 2020-11-24. A light transformer for speech-to-intent applications. https://arxiv.org/abs/2011.12221

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